{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "PDR Xception Testing.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aldnoahh/plant-disease-recognition/blob/master/PDR_Xception_Testing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QeWLiMnG0_uu",
        "colab_type": "text"
      },
      "source": [
        "#Plant Disease Recognition using Xception on modified version of PlantVillage Dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cj7pw9-JwAh3",
        "colab_type": "text"
      },
      "source": [
        "Importing necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RZFTFusEcsec",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ed4374f9-9e7c-4314-e263-33455dd71bbf"
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.3.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a4s4g3_Bcwu8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.layers import Input, Dense, Flatten\n",
        "from tensorflow.keras.applications.xception import Xception as PretrainedModel, preprocess_input\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import SGD, Adam\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "from glob import glob\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import sys, os"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zhXZ3Y9WwGdR",
        "colab_type": "text"
      },
      "source": [
        "Downloading and unzipping the modified dataset available on Gdrive. If you don`t have gdown module, install it using pip."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EPxnx4LVc_fW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!gdown --id 1Mj6wsKBZN2ycAyyIMs2lI361deuCJqBI --output pv0.zip\n",
        "!unzip pv0.zip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ngDbRT7iwdJi",
        "colab_type": "text"
      },
      "source": [
        "Check if the folder has been unzipped."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V7NhBksadZHd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a8b87cce-4563-4c1c-a6f6-f8b833fbd851"
      },
      "source": [
        "!ls"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "pv0  pv0.zip  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jOLHCEL1wiUd",
        "colab_type": "text"
      },
      "source": [
        "Setting up path for datagenerators from keras"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xu1nf5xkeiXs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a06d04a1-4675-4a09-d6fb-55c4dd1391f4"
      },
      "source": [
        "train_path = '/content/pv0/train'\n",
        "valid_path = '/content/pv0/test'\n",
        "# useful for getting number of files\n",
        "image_files = glob(train_path + '/*/*.JPG')\n",
        "valid_image_files = glob(valid_path + '/*/*.JPG')\n",
        "# useful for getting number of classes\n",
        "folders = glob(train_path + '/*')\n",
        "len(folders)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "38"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a-eZ_Jwnw22H",
        "colab_type": "text"
      },
      "source": [
        "Specify input image size."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pS1RfyFueujP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "IMAGE_SIZE = [256, 256]"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tt6ccnhhfJ6y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "2bf17832-a786-4b2a-da72-32fa46c60737"
      },
      "source": [
        "#sneek peek at a random image\n",
        "plt.imshow(image.load_img(np.random.choice(image_files)))\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W6g4APyRxBvP",
        "colab_type": "text"
      },
      "source": [
        "Configuring the pretrainned model as per our needs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mz1weoVCfN0-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "d53c0c0a-12bf-4078-f6ea-061c30c6566d"
      },
      "source": [
        "ptm = PretrainedModel(\n",
        "    input_shape=IMAGE_SIZE + [3],\n",
        "    weights='imagenet',\n",
        "    include_top=False)\n",
        "# freeze pretrained model weights\n",
        "ptm.trainable = False"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/xception/xception_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "83689472/83683744 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YkxxS9zifoR3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "K = len(folders) # number of classes\n",
        "\n",
        "#model definition\n",
        "x = Flatten()(ptm.output)\n",
        "x= BatchNormalization()(x)\n",
        "x= Dense(512,activation='relu')(x)\n",
        "x = Dense(K, activation='softmax')(x)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TqzghjxNftgX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# create a model object\n",
        "model = Model(inputs=ptm.input, outputs=x)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U8EO7sLIft1r",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4f64c0ef-903f-40bb-8356-a864689c67ae"
      },
      "source": [
        "# view the structure of the model\n",
        "model.summary()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 256, 256, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "block1_conv1 (Conv2D)           (None, 127, 127, 32) 864         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "block1_conv1_bn (BatchNormaliza (None, 127, 127, 32) 128         block1_conv1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block1_conv1_act (Activation)   (None, 127, 127, 32) 0           block1_conv1_bn[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block1_conv2 (Conv2D)           (None, 125, 125, 64) 18432       block1_conv1_act[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block1_conv2_bn (BatchNormaliza (None, 125, 125, 64) 256         block1_conv2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block1_conv2_act (Activation)   (None, 125, 125, 64) 0           block1_conv2_bn[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block2_sepconv1 (SeparableConv2 (None, 125, 125, 128 8768        block1_conv2_act[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block2_sepconv1_bn (BatchNormal (None, 125, 125, 128 512         block2_sepconv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block2_sepconv2_act (Activation (None, 125, 125, 128 0           block2_sepconv1_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2_sepconv2 (SeparableConv2 (None, 125, 125, 128 17536       block2_sepconv2_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block2_sepconv2_bn (BatchNormal (None, 125, 125, 128 512         block2_sepconv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d (Conv2D)                 (None, 63, 63, 128)  8192        block1_conv2_act[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block2_pool (MaxPooling2D)      (None, 63, 63, 128)  0           block2_sepconv2_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization (BatchNorma (None, 63, 63, 128)  512         conv2d[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "add (Add)                       (None, 63, 63, 128)  0           block2_pool[0][0]                \n",
            "                                                                 batch_normalization[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block3_sepconv1_act (Activation (None, 63, 63, 128)  0           add[0][0]                        \n",
            "__________________________________________________________________________________________________\n",
            "block3_sepconv1 (SeparableConv2 (None, 63, 63, 256)  33920       block3_sepconv1_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block3_sepconv1_bn (BatchNormal (None, 63, 63, 256)  1024        block3_sepconv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block3_sepconv2_act (Activation (None, 63, 63, 256)  0           block3_sepconv1_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3_sepconv2 (SeparableConv2 (None, 63, 63, 256)  67840       block3_sepconv2_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block3_sepconv2_bn (BatchNormal (None, 63, 63, 256)  1024        block3_sepconv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 32, 32, 256)  32768       add[0][0]                        \n",
            "__________________________________________________________________________________________________\n",
            "block3_pool (MaxPooling2D)      (None, 32, 32, 256)  0           block3_sepconv2_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, 32, 32, 256)  1024        conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "add_1 (Add)                     (None, 32, 32, 256)  0           block3_pool[0][0]                \n",
            "                                                                 batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block4_sepconv1_act (Activation (None, 32, 32, 256)  0           add_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block4_sepconv1 (SeparableConv2 (None, 32, 32, 728)  188672      block4_sepconv1_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block4_sepconv1_bn (BatchNormal (None, 32, 32, 728)  2912        block4_sepconv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block4_sepconv2_act (Activation (None, 32, 32, 728)  0           block4_sepconv1_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4_sepconv2 (SeparableConv2 (None, 32, 32, 728)  536536      block4_sepconv2_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block4_sepconv2_bn (BatchNormal (None, 32, 32, 728)  2912        block4_sepconv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 16, 16, 728)  186368      add_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block4_pool (MaxPooling2D)      (None, 16, 16, 728)  0           block4_sepconv2_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, 16, 16, 728)  2912        conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "add_2 (Add)                     (None, 16, 16, 728)  0           block4_pool[0][0]                \n",
            "                                                                 batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block5_sepconv1_act (Activation (None, 16, 16, 728)  0           add_2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block5_sepconv1 (SeparableConv2 (None, 16, 16, 728)  536536      block5_sepconv1_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block5_sepconv1_bn (BatchNormal (None, 16, 16, 728)  2912        block5_sepconv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block5_sepconv2_act (Activation (None, 16, 16, 728)  0           block5_sepconv1_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5_sepconv2 (SeparableConv2 (None, 16, 16, 728)  536536      block5_sepconv2_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block5_sepconv2_bn (BatchNormal (None, 16, 16, 728)  2912        block5_sepconv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block5_sepconv3_act (Activation (None, 16, 16, 728)  0           block5_sepconv2_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5_sepconv3 (SeparableConv2 (None, 16, 16, 728)  536536      block5_sepconv3_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block5_sepconv3_bn (BatchNormal (None, 16, 16, 728)  2912        block5_sepconv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "add_3 (Add)                     (None, 16, 16, 728)  0           block5_sepconv3_bn[0][0]         \n",
            "                                                                 add_2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block6_sepconv1_act (Activation (None, 16, 16, 728)  0           add_3[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block6_sepconv1 (SeparableConv2 (None, 16, 16, 728)  536536      block6_sepconv1_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block6_sepconv1_bn (BatchNormal (None, 16, 16, 728)  2912        block6_sepconv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block6_sepconv2_act (Activation (None, 16, 16, 728)  0           block6_sepconv1_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6_sepconv2 (SeparableConv2 (None, 16, 16, 728)  536536      block6_sepconv2_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block6_sepconv2_bn (BatchNormal (None, 16, 16, 728)  2912        block6_sepconv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block6_sepconv3_act (Activation (None, 16, 16, 728)  0           block6_sepconv2_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6_sepconv3 (SeparableConv2 (None, 16, 16, 728)  536536      block6_sepconv3_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block6_sepconv3_bn (BatchNormal (None, 16, 16, 728)  2912        block6_sepconv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "add_4 (Add)                     (None, 16, 16, 728)  0           block6_sepconv3_bn[0][0]         \n",
            "                                                                 add_3[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block7_sepconv1_act (Activation (None, 16, 16, 728)  0           add_4[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block7_sepconv1 (SeparableConv2 (None, 16, 16, 728)  536536      block7_sepconv1_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block7_sepconv1_bn (BatchNormal (None, 16, 16, 728)  2912        block7_sepconv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block7_sepconv2_act (Activation (None, 16, 16, 728)  0           block7_sepconv1_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block7_sepconv2 (SeparableConv2 (None, 16, 16, 728)  536536      block7_sepconv2_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block7_sepconv2_bn (BatchNormal (None, 16, 16, 728)  2912        block7_sepconv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block7_sepconv3_act (Activation (None, 16, 16, 728)  0           block7_sepconv2_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block7_sepconv3 (SeparableConv2 (None, 16, 16, 728)  536536      block7_sepconv3_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block7_sepconv3_bn (BatchNormal (None, 16, 16, 728)  2912        block7_sepconv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "add_5 (Add)                     (None, 16, 16, 728)  0           block7_sepconv3_bn[0][0]         \n",
            "                                                                 add_4[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block8_sepconv1_act (Activation (None, 16, 16, 728)  0           add_5[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block8_sepconv1 (SeparableConv2 (None, 16, 16, 728)  536536      block8_sepconv1_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block8_sepconv1_bn (BatchNormal (None, 16, 16, 728)  2912        block8_sepconv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block8_sepconv2_act (Activation (None, 16, 16, 728)  0           block8_sepconv1_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block8_sepconv2 (SeparableConv2 (None, 16, 16, 728)  536536      block8_sepconv2_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block8_sepconv2_bn (BatchNormal (None, 16, 16, 728)  2912        block8_sepconv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block8_sepconv3_act (Activation (None, 16, 16, 728)  0           block8_sepconv2_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block8_sepconv3 (SeparableConv2 (None, 16, 16, 728)  536536      block8_sepconv3_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block8_sepconv3_bn (BatchNormal (None, 16, 16, 728)  2912        block8_sepconv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "add_6 (Add)                     (None, 16, 16, 728)  0           block8_sepconv3_bn[0][0]         \n",
            "                                                                 add_5[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block9_sepconv1_act (Activation (None, 16, 16, 728)  0           add_6[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block9_sepconv1 (SeparableConv2 (None, 16, 16, 728)  536536      block9_sepconv1_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block9_sepconv1_bn (BatchNormal (None, 16, 16, 728)  2912        block9_sepconv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block9_sepconv2_act (Activation (None, 16, 16, 728)  0           block9_sepconv1_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block9_sepconv2 (SeparableConv2 (None, 16, 16, 728)  536536      block9_sepconv2_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block9_sepconv2_bn (BatchNormal (None, 16, 16, 728)  2912        block9_sepconv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block9_sepconv3_act (Activation (None, 16, 16, 728)  0           block9_sepconv2_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block9_sepconv3 (SeparableConv2 (None, 16, 16, 728)  536536      block9_sepconv3_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block9_sepconv3_bn (BatchNormal (None, 16, 16, 728)  2912        block9_sepconv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "add_7 (Add)                     (None, 16, 16, 728)  0           block9_sepconv3_bn[0][0]         \n",
            "                                                                 add_6[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block10_sepconv1_act (Activatio (None, 16, 16, 728)  0           add_7[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block10_sepconv1 (SeparableConv (None, 16, 16, 728)  536536      block10_sepconv1_act[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block10_sepconv1_bn (BatchNorma (None, 16, 16, 728)  2912        block10_sepconv1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block10_sepconv2_act (Activatio (None, 16, 16, 728)  0           block10_sepconv1_bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block10_sepconv2 (SeparableConv (None, 16, 16, 728)  536536      block10_sepconv2_act[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block10_sepconv2_bn (BatchNorma (None, 16, 16, 728)  2912        block10_sepconv2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block10_sepconv3_act (Activatio (None, 16, 16, 728)  0           block10_sepconv2_bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block10_sepconv3 (SeparableConv (None, 16, 16, 728)  536536      block10_sepconv3_act[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block10_sepconv3_bn (BatchNorma (None, 16, 16, 728)  2912        block10_sepconv3[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "add_8 (Add)                     (None, 16, 16, 728)  0           block10_sepconv3_bn[0][0]        \n",
            "                                                                 add_7[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block11_sepconv1_act (Activatio (None, 16, 16, 728)  0           add_8[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block11_sepconv1 (SeparableConv (None, 16, 16, 728)  536536      block11_sepconv1_act[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block11_sepconv1_bn (BatchNorma (None, 16, 16, 728)  2912        block11_sepconv1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block11_sepconv2_act (Activatio (None, 16, 16, 728)  0           block11_sepconv1_bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block11_sepconv2 (SeparableConv (None, 16, 16, 728)  536536      block11_sepconv2_act[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block11_sepconv2_bn (BatchNorma (None, 16, 16, 728)  2912        block11_sepconv2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block11_sepconv3_act (Activatio (None, 16, 16, 728)  0           block11_sepconv2_bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block11_sepconv3 (SeparableConv (None, 16, 16, 728)  536536      block11_sepconv3_act[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block11_sepconv3_bn (BatchNorma (None, 16, 16, 728)  2912        block11_sepconv3[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "add_9 (Add)                     (None, 16, 16, 728)  0           block11_sepconv3_bn[0][0]        \n",
            "                                                                 add_8[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block12_sepconv1_act (Activatio (None, 16, 16, 728)  0           add_9[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block12_sepconv1 (SeparableConv (None, 16, 16, 728)  536536      block12_sepconv1_act[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block12_sepconv1_bn (BatchNorma (None, 16, 16, 728)  2912        block12_sepconv1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block12_sepconv2_act (Activatio (None, 16, 16, 728)  0           block12_sepconv1_bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block12_sepconv2 (SeparableConv (None, 16, 16, 728)  536536      block12_sepconv2_act[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block12_sepconv2_bn (BatchNorma (None, 16, 16, 728)  2912        block12_sepconv2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block12_sepconv3_act (Activatio (None, 16, 16, 728)  0           block12_sepconv2_bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block12_sepconv3 (SeparableConv (None, 16, 16, 728)  536536      block12_sepconv3_act[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block12_sepconv3_bn (BatchNorma (None, 16, 16, 728)  2912        block12_sepconv3[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "add_10 (Add)                    (None, 16, 16, 728)  0           block12_sepconv3_bn[0][0]        \n",
            "                                                                 add_9[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block13_sepconv1_act (Activatio (None, 16, 16, 728)  0           add_10[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "block13_sepconv1 (SeparableConv (None, 16, 16, 728)  536536      block13_sepconv1_act[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block13_sepconv1_bn (BatchNorma (None, 16, 16, 728)  2912        block13_sepconv1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block13_sepconv2_act (Activatio (None, 16, 16, 728)  0           block13_sepconv1_bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block13_sepconv2 (SeparableConv (None, 16, 16, 1024) 752024      block13_sepconv2_act[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block13_sepconv2_bn (BatchNorma (None, 16, 16, 1024) 4096        block13_sepconv2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 8, 8, 1024)   745472      add_10[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "block13_pool (MaxPooling2D)     (None, 8, 8, 1024)   0           block13_sepconv2_bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, 8, 8, 1024)   4096        conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "add_11 (Add)                    (None, 8, 8, 1024)   0           block13_pool[0][0]               \n",
            "                                                                 batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block14_sepconv1 (SeparableConv (None, 8, 8, 1536)   1582080     add_11[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "block14_sepconv1_bn (BatchNorma (None, 8, 8, 1536)   6144        block14_sepconv1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block14_sepconv1_act (Activatio (None, 8, 8, 1536)   0           block14_sepconv1_bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block14_sepconv2 (SeparableConv (None, 8, 8, 2048)   3159552     block14_sepconv1_act[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block14_sepconv2_bn (BatchNorma (None, 8, 8, 2048)   8192        block14_sepconv2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block14_sepconv2_act (Activatio (None, 8, 8, 2048)   0           block14_sepconv2_bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "flatten (Flatten)               (None, 131072)       0           block14_sepconv2_act[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, 131072)       524288      flatten[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 512)          67109376    batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 38)           19494       dense[0][0]                      \n",
            "==================================================================================================\n",
            "Total params: 88,514,638\n",
            "Trainable params: 67,391,014\n",
            "Non-trainable params: 21,123,624\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tYty5eS-U9gY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c0013a88-e83d-40f1-b2d3-a4f5154f4614"
      },
      "source": [
        "#view the number of layers in the model\n",
        "len(model.layers)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "136"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wKC5ilt4hc1K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# create an instance of ImageDataGenerator\n",
        "#Keras generators returns one-hot encoded labels and provides data augmentation.\n",
        "gen_train = ImageDataGenerator(\n",
        "  rotation_range=90,\n",
        "  width_shift_range=0.1,\n",
        "  height_shift_range=0.1,\n",
        "  shear_range=0.1,\n",
        "  zoom_range=0.2,\n",
        "  horizontal_flip=True,\n",
        "  preprocessing_function=preprocess_input\n",
        ")\n",
        "\n",
        "gen_test = ImageDataGenerator(\n",
        "  preprocessing_function=preprocess_input\n",
        ")"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SgtuBIoThuZc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "47847a53-ecdb-4d22-90d6-2ec9cf7fd138"
      },
      "source": [
        "#batch size is the number of examples that are run through the model at once.\n",
        "batch_size = 300\n",
        "\n",
        "# create generators\n",
        "train_generator = gen_train.flow_from_directory(\n",
        "  train_path,\n",
        "  shuffle=True,\n",
        "  target_size=IMAGE_SIZE,\n",
        "  batch_size=batch_size,\n",
        ")\n",
        "valid_generator = gen_test.flow_from_directory(\n",
        "  valid_path,\n",
        "  target_size=IMAGE_SIZE,\n",
        "  batch_size=batch_size,\n",
        ")"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 46141 images belonging to 38 classes.\n",
            "Found 8162 images belonging to 38 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vXvEj-1byMah",
        "colab_type": "text"
      },
      "source": [
        "Since Keras no longer provides some metrics within itself, so we define those metrics ourselves. Here, we are defining F1_score, Precision and Recall."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vRsYGkdM3cTR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras import backend as Ke\n",
        "\n",
        "def recall_m(y_true, y_pred):\n",
        "    true_positives = Ke.sum(Ke.round(Ke.clip(y_true * y_pred, 0, 1)))\n",
        "    possible_positives = Ke.sum(Ke.round(Ke.clip(y_true, 0, 1)))\n",
        "    recall = true_positives / (possible_positives + Ke.epsilon())\n",
        "    return recall\n",
        "\n",
        "def precision_m(y_true, y_pred):\n",
        "    true_positives = Ke.sum(Ke.round(Ke.clip(y_true * y_pred, 0, 1)))\n",
        "    predicted_positives = Ke.sum(Ke.round(Ke.clip(y_pred, 0, 1)))\n",
        "    precision = true_positives / (predicted_positives + Ke.epsilon())\n",
        "    return precision\n",
        "\n",
        "def f1_m(y_true, y_pred):\n",
        "    precision = precision_m(y_true, y_pred)\n",
        "    recall = recall_m(y_true, y_pred)\n",
        "    return 2*((precision*recall)/(precision+recall+Ke.epsilon()))"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3LvAj8U_yg7Y",
        "colab_type": "text"
      },
      "source": [
        "This block is for creating a lr scheduler, since the lr scheduler was not as effective as using adam directly, it is left for experimentation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j0MqLWki7xxg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# from keras.optimizers import SGD\n",
        "# import math\n",
        "# def step_decay(epoch):\n",
        "# \tinitial_lrate = 1e-4\n",
        "# \tdrop = 0.5\n",
        "# \tepochs_drop = 10.0\n",
        "# \tlrate = initial_lrate * math.pow(drop, math.floor((1+epoch)/epochs_drop))\n",
        "# \treturn lrate\n",
        "# sgd = SGD(lr=0.0, momentum=0.9)\n",
        "# # learning schedule callback\n",
        "# from keras.callbacks import LearningRateScheduler\n",
        "# lrate = LearningRateScheduler(step_decay)\n",
        "# callbacks_list = [lrate]"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a_JS4tg1y17w",
        "colab_type": "text"
      },
      "source": [
        "Compiling our model with loss, optimizer and metrics (including our custom defined ones)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I5pDP0IStBZp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(\n",
        "  loss='categorical_crossentropy',\n",
        "  optimizer='adam',\n",
        "  metrics=['accuracy',f1_m,precision_m, recall_m]\n",
        ")"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EaUJj4D0y_VU",
        "colab_type": "text"
      },
      "source": [
        "The fit function is called for starting our training. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rlwkypTAsqW2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "outputId": "cfc1b157-9b83-4cbb-b95a-4a7c816f1fcb"
      },
      "source": [
        "# fit the model\n",
        "r = model.fit(\n",
        "  train_generator,\n",
        "  validation_data=valid_generator,\n",
        "  epochs=5,\n",
        "  steps_per_epoch=int(np.ceil(len(image_files) / batch_size)),\n",
        "  validation_steps=int(np.ceil(len(valid_image_files) / batch_size)),\n",
        ")"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "150/150 [==============================] - 1037s 7s/step - loss: 6.3383 - accuracy: 0.8095 - f1_m: 0.8095 - precision_m: 0.8098 - recall_m: 0.8092 - val_loss: 2.8122 - val_accuracy: 0.8521 - val_f1_m: 0.8521 - val_precision_m: 0.8527 - val_recall_m: 0.8515\n",
            "Epoch 2/5\n",
            "150/150 [==============================] - 1042s 7s/step - loss: 2.8107 - accuracy: 0.8784 - f1_m: 0.8785 - precision_m: 0.8791 - recall_m: 0.8780 - val_loss: 2.0386 - val_accuracy: 0.8835 - val_f1_m: 0.8838 - val_precision_m: 0.8847 - val_recall_m: 0.8830\n",
            "Epoch 3/5\n",
            "150/150 [==============================] - 1045s 7s/step - loss: 1.6748 - accuracy: 0.8928 - f1_m: 0.8933 - precision_m: 0.8951 - recall_m: 0.8915 - val_loss: 1.3789 - val_accuracy: 0.8998 - val_f1_m: 0.9008 - val_precision_m: 0.9033 - val_recall_m: 0.8984\n",
            "Epoch 4/5\n",
            "150/150 [==============================] - 1045s 7s/step - loss: 1.2438 - accuracy: 0.8954 - f1_m: 0.8968 - precision_m: 0.9013 - recall_m: 0.8924 - val_loss: 1.3851 - val_accuracy: 0.8925 - val_f1_m: 0.8945 - val_precision_m: 0.8998 - val_recall_m: 0.8893\n",
            "Epoch 5/5\n",
            "150/150 [==============================] - 1049s 7s/step - loss: 0.8378 - accuracy: 0.9001 - f1_m: 0.9023 - precision_m: 0.9104 - recall_m: 0.8943 - val_loss: 1.0098 - val_accuracy: 0.9027 - val_f1_m: 0.9042 - val_precision_m: 0.9085 - val_recall_m: 0.8999\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tM5UwYJXzLiL",
        "colab_type": "text"
      },
      "source": [
        "Saving our Model in HD5 format."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dOhj9StKbLPC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2f2107ee-8d9f-4f33-f6ae-9eb57c4ddfe1"
      },
      "source": [
        "model.save(\"model.h5\")\n",
        "print(\"Saved model to disk\")"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saved model to disk\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tL1gKZi_zSz2",
        "colab_type": "text"
      },
      "source": [
        "Graphs for our metrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2oa3tHhOSC8J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "740df7aa-48a5-4358-bbcf-96c2c1b736c1"
      },
      "source": [
        "# loss\n",
        "plt.plot(r.history['loss'], label='train loss')\n",
        "plt.plot(r.history['val_loss'], label='val loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T3urB6c6SDaU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "18f4c6fb-b444-4abf-82f9-02259d130e0b"
      },
      "source": [
        "# accuracies\n",
        "plt.plot(r.history['accuracy'], label='train acc')\n",
        "plt.plot(r.history['val_accuracy'], label='val acc')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-yt89FJw-_6M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "0766502e-94f7-4dd9-9d29-6696666bb275"
      },
      "source": [
        "# f1_score\n",
        "plt.plot(r.history['f1_m'], label='train f1_m')\n",
        "plt.plot(r.history['val_f1_m'], label='val f1_m')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KxkOwoIHFXJa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "8d4391d2-43e9-4e6b-88fd-af39a52f37bf"
      },
      "source": [
        "# precision\n",
        "plt.plot(r.history['precision_m'], label='train precision_m')\n",
        "plt.plot(r.history['val_precision_m'], label='val precision_m')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gd_sJAJ2FX0S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "de5ec328-634a-4308-c6cd-89bdb5561f95"
      },
      "source": [
        "# recall\n",
        "plt.plot(r.history['recall_m'], label='train recall_m')\n",
        "plt.plot(r.history['val_recall_m'], label='val recall_m')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oATCaclAzgYy",
        "colab_type": "text"
      },
      "source": [
        "Next we evaluate the model on our test set again."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "KXJFLjk8tNad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "e2ec5dd7-2d50-4afa-aed3-9efd98167b16"
      },
      "source": [
        "#Load saved model from training\n",
        "from keras.models import load_model\n",
        "amod= load_model('/content/model.h5')\n",
        "# evaluate the model\n",
        "valid_generator = gen_test.flow_from_directory(valid_path,target_size=IMAGE_SIZE,batch_size=batch_size,)\n",
        "loss, accuracy, f1_score, precision, recall = amod.evaluate(valid_generator, steps=int(np.ceil(len(valid_image_files)/ batch_size)))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 8162 images belonging to 38 classes.\n",
            "27/27 [==============================] - 100s 4s/step - loss: 1.0163 - accuracy: 0.9025 - f1_m: 0.9040 - precision_m: 0.9084 - recall_m: 0.8996\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x7bbYfpCzoDw",
        "colab_type": "text"
      },
      "source": [
        "Printing our metrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "IB5Lnaf7tNak",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "3bba9d1b-03bb-493d-bbfd-4a6bb9e69b68"
      },
      "source": [
        "print('loss     : ',loss)\n",
        "print('accuracy : ',accuracy)\n",
        "print('f1_score :',f1_score)\n",
        "print('precision:',precision)\n",
        "print('recall   :',recall)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "loss     :  1.0162622928619385\n",
            "accuracy :  0.9024691581726074\n",
            "f1_score : 0.9039744734764099\n",
            "precision: 0.9083781838417053\n",
            "recall   : 0.8996296525001526\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}